AIML Architect

We are seeking an experienced AI/ML Architect with strong expertise in machine learning system design production model deployment and hands-on coding ability. The ideal candidate will be highly skilled in architecting end-to-end ML solutions conducting code reviews across ML pipelines and delivering scalable predictive analytics systems for enterprise clients. This is a hands-on architecture role requiring both technical depth and delivery leadership.

Key Responsibilities:

• Design develop and maintain end-to-end ML architectures covering data ingestion feature engineering model training deployment and monitoring.
• Perform code reviews across ML pipelines model implementations and deployment scripts to maintain engineering quality standards.
• Build and deploy production machine learning models using frameworks such as LightGBM XGBoost scikit-learn PyTorch and TensorFlow.
• Implement MLOps practices including model versioning monitoring retraining pipelines and drift detection.
• Optimize model performance diagnose data quality issues and resolve production model degradation.
• Translate ambiguous client problem statements into technically sound deliverable ML solutions.
• Lead and mentor a team of ML engineers and data scientists establishing coding and validation standards.
• Act as technical authority in client discussions solution workshops and pre-sales engagements.
• Scope and estimate new ML opportunities and support proposals and RFP responses.
• Working under a dynamic agile based environment.
• Strictly adhere to scrum framework and guidelines.
• Coordinate with multiple development teams.

Qualifications :

Required Skills & Experience:

• Overall experience: 10 Years
• 6 years of professional experience in machine learning data science or applied AI with 3 years in an architect or technical lead capacity.
• Strong hands-on coding proficiency in Python with production ML framework experience.
• Demonstrated experience taking models from prototype to production not limited to POC or research work.
• Proven ability to conduct code reviews across ML pipelines data engineering and model deployment.
• Hands-on experience with classical ML techniques including supervised learning anomaly detection time-series analysis and imbalanced classification.
• Working knowledge of MLOps tooling and production model lifecycle management.
• Cloud platform experience (Azure AWS or GCP) including ML services and deployment infrastructure.
• Proficiency with SQL and large-scale data processing.
• Familiarity with version control systems (Git SVN etc.).
• Strong problem-solving skills and ability to work independently.

Preferred Skills:

• Experience with predictive maintenance hardware failure prediction or IoT/telemetry data.
• Exposure to log analytics observability data or large-scale unstructured data processing.
• Experience with Databricks Spark or similar distributed data platforms.
• Knowledge of REST API integration for model serving and inference endpoints.
• Client-facing consulting or services delivery background.
• Advanced degree (MS or PhD) in Computer Science Statistics Applied Mathematics or related field.
• Experience in agile development environments.

Remote Work :

No

Employment Type :

Full-time